1. Field of the Invention
The present invention generally relates to methods of designing autonomous mobile robots and more specifically relates to the prediction of the sensory consequences of movement, learning affordances using neural methods, and exploitation of the natural system dynamics to simplify computation and robot control in autonomous robot explorers.
2. Description of the Prior Art
Technology has generally been aimed to make human life easier by taking on the burden of human tasks, or performing tasks that humans cannot perform due to physical constraints. In turn, robots have, and continue to be, developed that are mobile and that have the ability to retrieve or report information in accordance with this technological trend. In other words, robots are being designed to relate to humans while providing them with life simplifying solutions. To meet this goal, robots are taking on forms similar to either humans or animals, for purposes of cognitively and emotionally relating with the technology, as well as for patterning the evolutionary success in mobility of humans or animals (also hereinafter sometimes collectively referred to as biological systems). Additionally, a major reason for choosing a legged form, particularly a two-legged humanoid form, is that humans have built a substantial environment based on human mobility needs. As such, robots using wheels and/or tracks generally do not meet the mobility needs for a variety of terrains where legged robots are generally more successful.
Bipedal locomotion over a flat, firm surface does not require visual or other type of distal sensory apparatus. However, if the environment is varied, vision or other type of distal sense is necessary to adjust gait in an anticipatory manner. Various visual cues are used by animals and humans to guide locomotion. These cues include cues that rely on or exploit the geometry of the environment: optic flow, stereopsis, depth from elevation and others as well as non-geometric cues such as the color, texture and surface patterns of the environment.
Movement of an observer (biological or otherwise) gives rise to motion parallax with objects in the environment. Light reflected or emitted by surfaces in the environment give rise to a pattern of luminosity changes on the observer's retina or imaging surface. This pattern of changing luminosity is optic flow. Optic flow is highly correlated with motion parallax. Through the examination of the optic flow field it is possible to determine time to contact, and structure of the environment, and the movement of the observer, including direction and rotation. The latter phenomenon is sometimes referred to as visual kinethesis in the literature. Scientific studies support the hypothesis that optic flow is essential for navigation of legged and flying biological systems in the environment.
Additional geometric visual cues include stereopsis and depth from elevation. Stereopsis is used to determine visual sensory data about the environment of a biological system by comparing two or more images from slightly different view points, the arrangement of human eyes being the archetypal example. Stereopsis can convey information about the size of an obstacle, although, in humans, it is apparently less important than other modalities for judging distance to an obstacle and is not an essential sensory factor for locomotion. Depth from elevation is yet another visual cue which operates under the assumption that the observer is kinematically connected to the obstacle being observed. Thus, if the observer is connected to a plane, obstacles closer to the observer will appear lower in the visual field than obstacles further away. This simple effect is exploited in biological systems to judge distances to points in the environment. However, these geometric cues alone, although helpful, are not sufficient for advanced locomotion.
Non-geometric visual cues mainly include texture, and color, but also encompass specular reflection or any other surface cue indicating the quality of a surface. These visual cues, when combined with geometric cues, can greatly enhance the success of locomotion as they assist the observer in anticipating surface characteristics. These visual cues aid biological systems in determining what characteristics a surface may exhibit, such as if a surface is slippery (e.g. ice).
The environment can ‘suggest’ desirable foot placement for navigating a region. FIG. 1 illustrates a stone walkway partially covered by ice and snow; the highlighted gray regions indicate the more favorable locations for foot placement within a reasonable proximity to the path of intended motion. The suggestion for a particular foot placement and the motor action necessary to accomplish this action is called an affordance.
Affordance encompasses how to perform an action but not the actual selection of such an action. The environment presents potential actions or affordances, and a choice is made as to which of the potential actions is the best pursuit. A person, seeing a mug, immediately perceives the many ways to grasp it, although there is no need for intermediate processing of ‘what’ the object is. Likewise, an animal, seeing a rock, immediately perceives a way to step over it, on top of it, or step around it depending upon the perceived size or shape of the given rock. Affordance perception includes the motor capabilities of the observer. It is also largely linked to learning abilities, for example, if a choice was made to step over a rock that turned out to be too large to successfully maneuver over, and as a result the animal fell, the animal would learn not to try to step over the rock, and use an alternative approach instead. Past research has managed to link affordances to neural substrates in the brain.
A key problem in the deployment of robots is that even the most agile robots, quadrupeds and especially bipeds, lack good affordance processes and can therefore be easily destabilized by obstacles. An affordance has the function of intelligent pattern matching: the current environment is matched to the set of possible motor actions that can be successfully executed by the animal or machine at a given time instant. This pattern matching can be quick and is superior in speed to methods that rely on algorithmicly driven geometric motion planning.
Vision can assist in stabilizing the subject's relationship to the environment, as well as being essential for navigation, route adjustment and planning. Without vision, the situation is worsened as the robot moves faster and has less time for appropriate planning based on alternate sensory cues (e.g. tactile). It is desirable to replicate animal visual sensory ability in robots to learn affordances and react to the surrounding environment using the previously described geometric and non-geometric methods. A method for achieving this must be resolved for robots to ensure successful mobility within a given environment.
Currently, there is surprisingly little work on the tight integration of vision and locomotion. Historically, the two fields have been addressed by largely separate groups of researchers.
Honda and Sony robots use vision for navigation (e.g. moving in the general direction of an obstacle). The Honda Asimo bipedal robot—“biped” for short—walks on two legs and can maneuver up and down stairs, turn, and walk with a reasonable gait. Sony has developed several generations of small quadruped robots called “Aibo”, but has also developed a biped robot. Sony's robots are viewed more as “content delivery devices” which playback media content developed by others, similar to a VCR, although exhibiting an appearance that is more human or animal in form.
Robotics has become a field yielding many important applications for the U.S. Military as well. However, past declassified reports that tracked robotic vehicles being used in the field during search and rescue operations following the World Trade Center collapse lacked the required mobility to adequately perform in such applications. Legged robots were recommended following this report for increased mobility.
As such, it is clear that there is a current and rapidly growing interest in legged robotic machines as well as a need for fast algorithms to provide these legged robots with visuomotor coordination.